Abnormal eye movement features in patients with depression: Preliminary findings based on eye tracking technology
Saccadic eye movement (SEM) has been considered a non-invasive potential biomarker for the diagnosis of depression in recent years, but its application is not yet mature. In this study, we used eye-tracking technology to identify the eye movements of patients with depression to develop a new method...
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Published in | General hospital psychiatry Vol. 84; pp. 25 - 30 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
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Elsevier Inc
01.09.2023
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Abstract | Saccadic eye movement (SEM) has been considered a non-invasive potential biomarker for the diagnosis of depression in recent years, but its application is not yet mature. In this study, we used eye-tracking technology to identify the eye movements of patients with depression to develop a new method for objectively identifying depression.
Thirty-six patients with depression as the depression group, while thirty-six matched healthy individuals as the control group were recruited and completed eye movement tests, including two tasks: the prosaccade task and the antisaccade task. iViewX RED 500 eye-tracking instruments from SMI were used to collect eye movement data for both groups.
In the prosaccade task, there was no difference between the depression and control groups(t = 0.019, P > 0.05). In general, with increasing angle, both groups showed significantly higher peak velocity (F = 81.72, P < 0.0001), higher mean velocity (F = 32.83, P = 0.000), and greater SEM amplitude (F = 24.23, P < 0.0001). In the antisaccade task, there were significant differences in correct rate (t = 3.219, P = 0.002) and mean velocity (F = 3.253, P < 0.05) between the depression group and the control group. In the anti-effect analysis, there were significant differences in correct rate (F = 67.44, P < 0.0001) and accuracy (F = 79.02, P < 0.0001) between the depression group and the control group. Both groups showed longer latency and worse correct rate and precision in the antisaccade task compared with the prosaccade task.
Patients with depression showed different eye movement features, which could be potential biomarkers for clinical identification. Further studies must validate these results with larger sample sizes and more clinical populations. |
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AbstractList | Saccadic eye movement (SEM) has been considered a non-invasive potential biomarker for the diagnosis of depression in recent years, but its application is not yet mature. In this study, we used eye-tracking technology to identify the eye movements of patients with depression to develop a new method for objectively identifying depression.BACKGROUNDSaccadic eye movement (SEM) has been considered a non-invasive potential biomarker for the diagnosis of depression in recent years, but its application is not yet mature. In this study, we used eye-tracking technology to identify the eye movements of patients with depression to develop a new method for objectively identifying depression.Thirty-six patients with depression as the depression group, while thirty-six matched healthy individuals as the control group were recruited and completed eye movement tests, including two tasks: the prosaccade task and the antisaccade task. iViewX RED 500 eye-tracking instruments from SMI were used to collect eye movement data for both groups.METHODSThirty-six patients with depression as the depression group, while thirty-six matched healthy individuals as the control group were recruited and completed eye movement tests, including two tasks: the prosaccade task and the antisaccade task. iViewX RED 500 eye-tracking instruments from SMI were used to collect eye movement data for both groups.In the prosaccade task, there was no difference between the depression and control groups(t = 0.019, P > 0.05). In general, with increasing angle, both groups showed significantly higher peak velocity (F = 81.72, P < 0.0001), higher mean velocity (F = 32.83, P = 0.000), and greater SEM amplitude (F = 24.23, P < 0.0001). In the antisaccade task, there were significant differences in correct rate (t = 3.219, P = 0.002) and mean velocity (F = 3.253, P < 0.05) between the depression group and the control group. In the anti-effect analysis, there were significant differences in correct rate (F = 67.44, P < 0.0001) and accuracy (F = 79.02, P < 0.0001) between the depression group and the control group. Both groups showed longer latency and worse correct rate and precision in the antisaccade task compared with the prosaccade task.RESULTSIn the prosaccade task, there was no difference between the depression and control groups(t = 0.019, P > 0.05). In general, with increasing angle, both groups showed significantly higher peak velocity (F = 81.72, P < 0.0001), higher mean velocity (F = 32.83, P = 0.000), and greater SEM amplitude (F = 24.23, P < 0.0001). In the antisaccade task, there were significant differences in correct rate (t = 3.219, P = 0.002) and mean velocity (F = 3.253, P < 0.05) between the depression group and the control group. In the anti-effect analysis, there were significant differences in correct rate (F = 67.44, P < 0.0001) and accuracy (F = 79.02, P < 0.0001) between the depression group and the control group. Both groups showed longer latency and worse correct rate and precision in the antisaccade task compared with the prosaccade task.Patients with depression showed different eye movement features, which could be potential biomarkers for clinical identification. Further studies must validate these results with larger sample sizes and more clinical populations.CONCLUSIONPatients with depression showed different eye movement features, which could be potential biomarkers for clinical identification. Further studies must validate these results with larger sample sizes and more clinical populations. Saccadic eye movement (SEM) has been considered a non-invasive potential biomarker for the diagnosis of depression in recent years, but its application is not yet mature. In this study, we used eye-tracking technology to identify the eye movements of patients with depression to develop a new method for objectively identifying depression. Thirty-six patients with depression as the depression group, while thirty-six matched healthy individuals as the control group were recruited and completed eye movement tests, including two tasks: the prosaccade task and the antisaccade task. iViewX RED 500 eye-tracking instruments from SMI were used to collect eye movement data for both groups. In the prosaccade task, there was no difference between the depression and control groups(t = 0.019, P > 0.05). In general, with increasing angle, both groups showed significantly higher peak velocity (F = 81.72, P < 0.0001), higher mean velocity (F = 32.83, P = 0.000), and greater SEM amplitude (F = 24.23, P < 0.0001). In the antisaccade task, there were significant differences in correct rate (t = 3.219, P = 0.002) and mean velocity (F = 3.253, P < 0.05) between the depression group and the control group. In the anti-effect analysis, there were significant differences in correct rate (F = 67.44, P < 0.0001) and accuracy (F = 79.02, P < 0.0001) between the depression group and the control group. Both groups showed longer latency and worse correct rate and precision in the antisaccade task compared with the prosaccade task. Patients with depression showed different eye movement features, which could be potential biomarkers for clinical identification. Further studies must validate these results with larger sample sizes and more clinical populations. |
Author | Gao, Dongmei Wang, Jieqiong Wang, Qingxiang Gao, Mingzhou Yu, Yanhong Xin, Rongrong |
Author_xml | – sequence: 1 givenname: Mingzhou surname: Gao fullname: Gao, Mingzhou organization: Innovation Research Institute of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, China – sequence: 2 givenname: Rongrong surname: Xin fullname: Xin, Rongrong organization: Qingdao Laoshan District Golden Key kindergarten, Qingdao, Shandong Province, China – sequence: 3 givenname: Qingxiang surname: Wang fullname: Wang, Qingxiang organization: School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China – sequence: 4 givenname: Dongmei surname: Gao fullname: Gao, Dongmei organization: College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, China – sequence: 5 givenname: Jieqiong surname: Wang fullname: Wang, Jieqiong organization: School of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, China – sequence: 6 givenname: Yanhong surname: Yu fullname: Yu, Yanhong email: yhysdutcm@163.com organization: College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, China |
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CitedBy_id | crossref_primary_10_1016_j_bspc_2024_106996 crossref_primary_10_3389_fpsyt_2024_1280935 crossref_primary_10_1007_s42979_025_03810_y crossref_primary_10_3389_fneur_2025_1555630 |
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Keywords | Biomarkers Depression Auxiliary diagnosis Eye-tracking Eye movement deficits |
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